Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm

Belarbi, Khaled; Titel, Faouzi; Bourebia, W.; Benmahammed, K.

doi:10.1016/j.engappai.2005.03.003

Cited by 62 publications

(29 citation statements)

References 16 publications

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“…To construct fuzzy systems using as less as possible fuzzy rules with guaranteed desired performances is a meaningful problem, which has attracted much attention for a long time in the fuzzy community [2][3][4][5]. Wan et al [6] introduced a computational geometry approach to determine the minimum number of rules required in building a fuzzy model to achieve a given approximation accuracy.…”

Section: Introductionmentioning

confidence: 99%

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Soukkou¹,

Leulmi²,

Khellaf³

2007

Optim Control Appl Methods

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SUMMARYDesigning an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA-PI). Simulations demonstrate that the proposed HAFSL and GA-PI algorithms have good generalization capabilities and robustness on the water bath temperature control system.

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Section: Introductionmentioning

confidence: 99%

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Soukkou¹,

Leulmi²,

Khellaf³

2007

Optim Control Appl Methods

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“…The defuzzification interface mixes and converts fuzzy membership functions into significant numerical outputs. Depending on the types of inference operations upon if-then rules, three types of fuzzy inference systems have been widely employed in various applications: Mamdani fuzzy models [9], Sugeno fuzzy models [10], and Tsukamoto fuzzy models [11]. The difference between these models is related to consequents of their fuzzy rules.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic

Karami¹,

Yousefi²,

Rezaei³

et al. 2012

The International Journal of Multiphysics

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“…The range of these values expresses the MF components in the FIS. Mamdani and Sugeno are the two practical FIS types which are used in several studies [21][22][23][24]. The main difference between these two fuzzy algorithms is based on the process complexity and the rule definition.…”

Section: Fuzzy Inference Systems (Fiss)mentioning

confidence: 99%

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Navidi

Landry

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, Global Positioning System (GPS) receivers are aided by some complementary radio navigation systems and Inertial Navigation Systems (INS) to obtain more accuracy and robustness in land vehicular navigation. Extended Kalman Filter (EKF) is an acceptable conventional method to estimate the position, the velocity, and the attitude of the navigation system when INS measurements are fused with GPS data. However, the usage of the low-cost Inertial Measurement Units (IMUs) based on the MicroElectro-Mechanical Systems (MEMS), for the land navigation systems, reduces the precision and stability of the navigation system due to their inherent errors. The main goal of this paper is to provide a new model for fusing low-cost IMU and GPS measurements. The proposed model is based on EKF aided by Fuzzy Inference Systems (FIS) as a promising method to solve the mentioned problems. This model considers the parameters of the measurement noise to adjust the measurement and noise process covariance. The simulation results show the efficiency of the proposed method to reduce the navigation system errors compared with EKF.

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Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm

Cited by 62 publications

References 16 publications

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

Modeling of the free convection heat transfer from an isothermal horizontal cylinder in a vertical channel via the fuzzy logic

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

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